An autonomous AI agent here to uncover waste and inefficiencies in government spending and policy decisions
Each year, the U.S. Congress introduces a large number of bills, many of which are complex and difficult for the general public to understand. Some bills are quickly approved, while others languish for extended periods, and the sheer volume of legislation makes it hard for citizens to stay informed. Additionally, the language used in these bills is often legalistic and inaccessible, further alienating the public from the legislative process. As a result, many citizens remain unaware of key legislation that impacts their lives.
While the immediate focus is on making it easier for the public to understand these bills, we are also tackling a bigger challenge: the complexity and inaccessibility of existing government systems. The infrastructure we've developed to scrape and present this data is complex, and we believe in the power of open-source solutions. By creating efficient, user-friendly tooling to extract and enrich government data, we aim to empower others to build even more public goods, creating a vibrant ecosystem of accessible, transparent government data that anyone can contribute to.
This repository serves as the data layer and framework for working with government data. It contains the tooling that powers our agent, and is designed to be flexible, allowing for the creation of various other agents that can build on top of this foundation. We hope this framework becomes the foundation for new ways to interact with and leverage government data, enabling innovative solutions and greater public engagement.
In order to achieve the vision following are actionable objectives:
- Transform DogeXBT into a highly effective agent that consistently delivers quality content.
- Enable the deployment of agents for specific government departments (e.g., DOJ, FDA), creating a swarm of specialized DogeXBT agents.
- Develop open-source infrastructure to support the operation of individual agents and entire swarms, making it accessible and scalable for broader use.
The project will be developed in distinct phases (or sprints), each building on the previous to establish a robust and scalable system. The focus is on creating a strong foundation while iterating based on feedback and evolving requirements.
- Set up infrastructure to process government bills.
- Scrape all Senate & House bills and analyze them to identify those that may be wasteful of government funds.
- Launch a website and social media presence on X (formerly Twitter) to share these bills.
- Launch a token.
- Make agent to reply to X replies,
- Make agent to reply to mentions.
- Highlight wasteful funding we have captured on the website.
- Configure agent to tweet 2-3 bills daily.
- CRON interval to scrape bills from the U.S. Congress API.
Note
Things evolve quickly. We want to keep the roadmap flexible and community driven. These are just ideas for now.
- Enhance the website to serve as a hub for exploring all scraped bills and provide a way for users to share them on X.
- Provide the dataset for others to use for research or building purposes.
- Enable interaction with the agent on X.
- Integrate the website with X so users can chat with the agent and share directly to X.
We welcome contributions from the community! If you're interested in helping us build DogeXBT.
- Node.js
v22.x- You can install it from the official website. pnpmis the package manager we use for this project. You can install it by runningnpm install -g pnpm.- Docker (optional) - I use OrbStack but you can use Docker Desktop or any other tool you prefer.
- Clone the repository.
- Run
pnpm installto install the project dependencies.
There are lot of things to build and improve in this project. You can look at the issues and see if there is anything you can help with or reach out on X and we can discuss how you can help.
This monorepo is structured as follows:
A Node.js application that scrapes data from the U.S. Congress API. We scrape a list of bills then process a bill each time to enrich it with additional data and run through a summarization process. The processing is done via inngest queues which makes it super easy to handle retry/failure logic and scale the processing. Below is the flow of the crawler:
graph LR
Crawler["Crawler"] --> ListBills["Scrape bills from API"]
ListBills --> |"Enqueue bill for processing"| Queue1["Queue"]
Queue1 --> BillProcessor["Bill enrichment"]
BillProcessor --> GetBill["Get bill details from API"]
BillProcessor --> FetchBillText["Scrape congress site for bill text"]
BillProcessor --> Summary["Summarize bill"]
GetBill --> DB["Database"]
FetchBillText --> DB
Summary --> DB
The service is deployed on Fly.io and runs when we trigger it.
Given the early stages this project we just trigger from initial crawl to congress API from CLI then queue the bills to our deployed crawler infra. Need to move the initial scrape to a CRON job.
This is based on Eliza framework. Currently it is a simple chatbot to which we feed a bill and it can help understand the bill. Given the early stage of the project and we are still tweaking our agent. The workflow today is we run the agent locally as a CLI tool and feed it the bills we want to understand then it generates the content.
Home for dogexbt.ai. A Next.js application.
This project is licensed under the MIT License - see the LICENSE file.